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dc.contributor.authorChua, David B.en_US
dc.contributor.authorKolaczyk, Eric D.en_US
dc.contributor.authorCrovella, Mark E.en_US
dc.date.accessioned2017-02-01T03:14:22Z
dc.date.available2017-02-01T03:14:22Z
dc.date.issued2006-12
dc.identifierhttp://www.cs.bu.edu/faculty/crovella/paper-archive/Network_Kriging.pdf
dc.identifier.citationDavid B Chua, Eric D Kolaczyk, Mark Crovella. 2006. "Network Kriging." IEEE Journal on Selected Areas in Communications, Special Issue on Sampling the Internet, Volume 24, pp. 2263 - 2272.
dc.identifier.otherhttps://arxiv.org/abs/math/0510013v2
dc.identifier.urihttps://hdl.handle.net/2144/20243
dc.description.abstractNetwork service providers and customers are often concerned with aggregate performance measures that span multiple network paths. Unfortunately, forming such network-wide measures can be difficult, due to the issues of scale involved. In particular, the number of paths grows too rapidly with the number of endpoints to make exhaustive measurement practical. As a result, it is of interest to explore the feasibility of methods that dramatically reduce the number of paths measured in such situations while maintaining acceptable accuracy. We cast the problem as one of statistical prediction—in the spirit of the so-called ‘kriging’ problem in spatial statistics—and show that end-to-end network properties may be accurately predicted in many cases using a surprisingly small set of carefully chosen paths. More precisely, we formulate a general framework for the prediction problem, propose a class of linear predictors for standard quantities of interest (e.g., averages, totals, differences) and show that linear algebraic methods of subset selection may be used to effectively choose which paths to measure. We characterize the performance of the resulting methods, both analytically and numerically. The success of our methods derives from the low effective rank of routing matrices as encountered in practice, which appears to be a new observation in its own right with potentially broad implications on network measurement generally.en_US
dc.format.extentp. 2263 - 2272en_US
dc.language.isoen_US
dc.relation.ispartofIEEE Journal on Selected Areas in Communications, Special Issue on Sampling the Internet
dc.subjectAlgorithmsen_US
dc.subjectMonitoringen_US
dc.subjectNetwork measurementsen_US
dc.subjectRouting matricesen_US
dc.subjectSamplingen_US
dc.subjectStatisticsen_US
dc.subjectStatistics theoryen_US
dc.subjectElectrical and electronic engineeringen_US
dc.subjectCommunications technologiesen_US
dc.subjectDistributed computingen_US
dc.subjectNetworking & telecommunicationsen_US
dc.titleNetwork krigingen_US
dc.typeArticleen_US
pubs.notescatid1: IM catid2: graphestimen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-group/Boston Universityen_US
pubs.organisational-group/Boston University/College of Arts & Sciencesen_US
pubs.organisational-group/Boston University/College of Arts & Sciences/Department of Computer Scienceen_US
pubs.organisational-group/Boston University/College of Arts & Sciences/Department of Mathematics & Statisticsen_US
dc.identifier.orcid0000-0002-5005-7019 (Crovella, Mark)


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